dace

Design and Analysis of Computer Experiments

Topics

package_ddace, design_and_analysis_of_computer_experiments

Specification

  • Alias: None

  • Arguments: None

Child Keywords:

Required/Optional

Description of Group

Dakota Keyword

Dakota Keyword Description

Required (Choose One)

DACE type

grid

Grid Sampling

random

Uses purely random Monte Carlo sampling to sample variables

oas

Orthogonal Array Sampling

lhs

Uses Latin Hypercube Sampling (LHS) to sample variables

oa_lhs

Orthogonal Array Latin Hypercube Sampling

box_behnken

Box-Behnken Design

central_composite

Central Composite Design

Optional

samples

Number of samples for sampling-based methods

Optional

seed

Seed of the random number generator

Optional

fixed_seed

Reuses the same seed value for multiple random sampling sets

Optional

main_effects

ANOVA

Optional

quality_metrics

Calculate metrics to assess the quality of quasi-Monte Carlo samples

Optional

variance_based_decomp

Activates global sensitivity analysis based on decomposition of response variance into contributions from variables

Optional

symbols

Number of replications in the sample set

Optional

model_pointer

Identifier for model block to be used by a method

Description

The Distributed Design and Analysis of Computer Experiments (DDACE) library provides the following DACE techniques:

  1. grid sampling ( grid)

  2. pure random sampling ( random)

  3. orthogonal array sampling ( oas)

  4. latin hypercube sampling ( lhs)

  5. orthogonal array latin hypercube sampling ( oa_lhs)

  6. Box-Behnken ( box_behnken)

  7. central composite design ( central_composite)

These methods all generate point sets that may be used to drive a set of computer experiments. Note that all of the DACE methods generated randomized designs, except for Box-Behnken and Central composite which are classical designs. That is, the grid sampling will generate a randomized grid, not what one typically thinks of as a grid of uniformly spaced points over a rectangular grid. Similar, the orthogonal array is a randomized version of an orthogonal array: it does not generate discrete, fixed levels.

In addition to the selection of the method, there are keywords that affect the method outputs:

  1. main_effects

  2. quality_metrics

  3. variance_based_decomp

And keywords that affect the sampling:

  1. fixed_seed

  2. symbols

  3. samples

  4. seed